A Semi-Global Matching method for large-scale light field images

Semi-Global Matching (SGM) is a robust method in traditional stereo matching. It maintains precise boundary with low computational cost. However, directly applying SGM to light field stereo matching degrades the results greatly due to the sparsity of support points. In this letter, we proposes a novel stereo matching approach for large-scale light field images. We observe that adding weak edges to support points efficiently stabilizes the depth propagation. Based on this observation, we apply a cross detector to obtain support points, and then we propagate the depth of support points to homogeneous region. By solving a semi-global energy minimization problem, the depth information can be well estimated from epipolar plane images. Besides, we introduce a new strategy to deal with occlusion. We iteratively sample the pixels under current disparity hypothesis and the consistency scores are aggregated by a weighted winnertake-all strategy. Our method allows for significant reduce of the disparity search space, the time is halved and the depth is more robust at the occurrence of occlusion. For every pixel, the calculation is based on a single EPI and locally independent. Implementation on GPU shows that our method can achieve state-of-art results with less computational cost.